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Suchi Saria, Phd Assistant Professor of Computer Science, Statistics, and Health Policy, Johns Hopkins University

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Suchi Saria, Phd Assistant Professor of Computer Science, Statistics, and Health Policy, Johns Hopkins University Biostatistics Seminars Winter 2017 Suchi Saria, PhD Assistant Professor of Computer Science, Statistics, and Health Policy, Johns Hopkins University Scalable Joint Models for Reliable Event Prediction: Application to Monitoring Adverse Events using Electronic Health Record Data Tuesday, 7 February 2017 3:30 pm – 4:30 pm - Purvis Hall, 1020 Pine Ave. West, Room 24 ALL ARE WELCOME Abstract: Many life-threatening adverse events such as sepsis and cardiac arrest are treatable if detected early. Towards this, one can leverage the vast number of longitudinal signals---e.g., repeated heart rate, respiratory rate, blood cell counts, creatinine measurements---that are already recorded by clinicians to track an individual's health. Motivated by this problem, we propose a reliable event prediction framework comprising two key innovations. First, we extend existing state-of-the-art in joint-modeling to tackle settings with large-scale, (potentially) correlated, high-dimensional multivariate longitudinal data. For this, we propose a flexible Bayesian nonparametric joint model along with scalable stochastic variational inference techniques for estimation. Second, we use a decision-theoretic approach to derive an optimal detector that trades-off the cost of delaying correct adverse-event detections against making incorrect assessments. On a challenging clinical dataset on patients admitted to an Intensive Care Unit, we see significant gains in early event-detection performance over state-of-the-art techniques. Bio: Suchi Saria is an assistant professor of computer science, (bio)statistics, and health policy at Johns Hopkins University. Her research interests are in statistical machine learning and "precision" healthcare. Specifically, her focus is in designing novel data-driven computing tools for optimizing care delivery. Her work is being used to drive electronic surveillance for reducing adverse events in the inpatient setting and to individualize disease management in complex, chronic diseases. She received her PhD from Stanford University with Prof. Daphne Koller. Her work has received recognition in the form of two cover articles in Science Translational Medicine (2010, 2015), paper awards by the Association for Uncertainty in Artificial Intelligence (2007) and the American Medical Informatics Association (2011), an Annual Scientific Award by the Society of Critical Care Medicine (2014), a Rambus Fellowship (2004-2010), an NSF Computing Innovation fellowship (2011), and competitive awards from the Gordon and Betty Moore Foundation (2013), and Google Research (2014). In 2015, she was selected by the IEEE Intelligent Systems to the ``AI's 10 to Watch'' list. In 2016, she was selected as a DARPA Young Faculty awardee and to Popular Science's ``Brilliant 10’’. www.mcgill.ca/epi-biostat-occh/news-events/seminars/biostatistics .
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